how to create a loop over a different set of variables and models in R

Question

code below first prints out lm for mpg ~ disp then for mpg ~ disp + wt. I would like to create another loop over the models (note that the second lm is my personalize model, and for the simplicity, we can assume it is lm). how can I loop over different models?

data("mtcars")

formulas <- list(
  mpg ~ disp,
  mpg ~ disp + wt
)

    models <- list(lm, lm)
    
    res <- vector("list", length = length(formulas))
    
    for(i in seq_along(formulas)){
      res[[i]] <- lm(formulas[[i]], data = mtcars)
    
    }
    res
    
    or
    
    lapply(formulas, lm, data = mtcars)

https://stackoverflow.com/questions/32231681/r-for-loop-for-regression-lmyx/32236702 might be useful :) — Jonny Phelps, Jun 08 '21 at 12:40
hello, thank you but it seems to be they only loop over the formulas not the models. — Seyma Kalay, Jun 08 '21 at 12:44

Ronak Shah · Accepted Answer · 2021-06-08T12:56:30.467

2

You may use nested lapply -

lapply(models, function(x) lapply(formulas, function(y) x(y, data = mtcars)))

edited Jun 08 '21 at 12:56

answered Jun 08 '21 at 12:46

Ronak Shah

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`mpg ~ disp` and ``mpg ~ disp + wt` should be printed twice as the length of moduls. – Seyma Kalay Jun 08 '21 at 12:54
1

See the updated answer. Is that what you want? – Ronak Shah Jun 08 '21 at 12:56

score 2 · Answer 2 · answered Jun 08 '21 at 12:52

I like to use tidyverse's purrr for such multi-model approaches:

pacman::p_load(dplyr, purrr)
data("mtcars")

d <- crossing(formula = c(mpg ~ disp, mpg ~ disp + wt),
              model = list("lm", "glm")) %>% 
  
  mutate(result = pmap(.l = list(model, formula),
                       .f = function(m, f) do.call(m, args = list(formula = f, data = substitute(mtcars)))))

score 2 · Answer 3 · answered Jun 08 '21 at 15:56

2

We could use outer in base R and should be fast

out <- c(outer(models, formulas, Vectorize(function(x, y) list(x(y, data = mtcars)))))

answered Jun 08 '21 at 15:56

akrun

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how to create a loop over a different set of variables and models in R

3 Answers3